Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

Author:

Toni Tina12,Welch David3,Strelkowa Natalja4,Ipsen Andreas5,Stumpf Michael P.H12

Affiliation:

1. Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College LondonLondon SW7 2AZ, UK

2. Institute of Mathematical Sciences, Imperial College LondonLondon SW7 2AZ, UK

3. Department of Epidemiology and Public Health, Imperial College LondonLondon SW7 2AZ, UK

4. Department of Bioengineering, Imperial College LondonLondon SW7 2AZ, UK

5. Department of Biomolecular Medicine, Imperial College LondonLondon SW7 2AZ, UK

Abstract

Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper, we discuss and apply an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC provides information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well-known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus.

Publisher

The Royal Society

Subject

Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biophysics,Biotechnology

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